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다변량 회귀
함수
mvregress | 다변량 선형 회귀 |
mvregresslike | Negative log-likelihood for multivariate regression |
polytool | Interactive polynomial fitting |
polyconf | Polynomial confidence intervals |
plsregress | Partial least-squares (PLS) regression |
예제 및 방법
- Set Up Multivariate Regression Problems
To fit a multivariate linear regression model using
mvregress
, you must set up your response matrix and design matrices in a particular way. - Multivariate General Linear Model
This example shows how to set up a multivariate general linear model for estimation using
mvregress
. - Fixed Effects Panel Model with Concurrent Correlation
This example shows how to perform panel data analysis using
mvregress
. - Longitudinal Analysis
This example shows how to perform longitudinal analysis using
mvregress
. - 부분 최소제곱 회귀 및 주성분 회귀
부분 최소제곱 회귀(PLSR) 및 주성분 회귀(PCR)를 적용하고 이 두 방법의 효과를 살펴봅니다.
개념
- Multivariate Linear Regression
Large, high-dimensional data sets are common in the modern era of computer-based instrumentation and electronic data storage.
- Estimation of Multivariate Regression Models
When you fit multivariate linear regression models using
mvregress
, you can use the optional name-value pair'algorithm','cwls'
to choose least squares estimation. - Partial Least Squares
Partial least squares (PLS) constructs new predictor variables as linear combinations of the original predictor variables, while considering the observed response values, leading to a parsimonious model with reliable predictive power.